1. Field of the Invention
The present invention relates to a system and method for the generation of forecasts. In particular the invention combines a neural network with a statistical model to produce more accurate forecasts.
2. Description of the Prior Art
Any organization, whether commercial, charitable, not for profit, or governmental, needs to plan its future activities. Planning necessarily requires estimation of future needs and demands. Thus, forecasting plays an important role in the planning process of any organization. The ability to accurately forecast or predict future conditions and needs is critical to the survival of many organizations. These organizations rely on forecasts so that they can allocate resources efficiently, balance workload against the forecasted demand and plan their operations to met needs and demands that will be placed upon them.
Most organizations require forecasts of volumes which are affected by historical trends over a wide variety of variables. Any entitlement program, service organization or sales force faces requirements for workload balancing based on projections of the number of customers or the volume of orders. These kinds of forecasts typically are dependent on trends in customer behavior. For instance, the United States Internal Revenue Service (IRS) plans the level of customer service at each geographical district office as many as five years in advance. The IRS provides services such as answering taxpayer questions, disseminating tax forms and providing publications and instructions for IRS representatives. To provide quality service, the IRS plans the utilization of resources. These resources include office space, conference areas, telephones, computers, personnel or other resources. Analysis of historical data shows that these services directly relate to the number of forms returned by taxpayers. For example, the level of service required for Form 1040 in a district office is related to the number of forms returned by the geographical region it serves. This relationship between service and return volume implies that the ability to forecast the volume of returns for specific forms can improve the accuracy of planning for services to be provided.
There are typically many factors affecting any one forecast. However, existing methods for solving this class of problems can only utilize a limited subset of the relevant factors.
There is a long-felt need in the art for producing accurate forecasts that utilize all applicable factors. There is also a long-felt need to produce forecasts that can be explained.